What is Customer Lifetime Value?

Customer Lifetime Value (CLV) describes the amount of revenue or profit a customer generates over his or her entire lifetime.

Many retailers optimize their customer acquisition strategies by trying to minimize how much they spend to acquire each customer (“cost per acquisition” or CAC”). When you understand the lifetime value of different customers, however, you can optimize more effectively for the long run. Rather than simply optimizing for CAC, you can look at the difference between CAC and CLV. After all, if one customer is 10x more valuable than another, it’s certainly worth spending a little more to acquire him!

The difference between Historical and Predictive CLV

Predictive
lifetime value projects what new customers will spend over their entire lifetime.

If you’re interested in using CLV to help optimize your advertising campaigns, you likely are more interested in predictive CLV analysis. For example, imagine you’re interested in optimizing your adwords spending by looking at both the cost per conversion along with the CLV of customers from a given campaign.

With historical CLV analysis, you’ll need to wait a couple years to know the CLV of those customers. With predictive CLV analysis, you’ll know the long-term value of those new customers right away.

Of course, predictive CLV is only useful in so far as the projections are accurate!

How CLV is calculated makes all the difference

CLV calculations vary wildly based on methodology. After all, the metric is a complex one to calculate - it includes not only the profits obtained so far from a customer, but expected profits in the future as well.

For this reason, it is important to "test" the metric. If we predict a customer will be worth $X in a year, we can check up on that and see how well the numbers perform.

Our approach: probabilistic marketing techniques

Over the past five years, academics have made profound strides in calculating lifetime value. Newer algorithms utilize the latest Bayesian probabilistic theories.

These new approaches of modeling recognize that all customers are different - and it is this recognition of what academics call "customer heterogeneity" that makes all the difference.

At a high level, the models work as follows:

Observe various individual-level buying patterns from the past - find the various customer stories in the data set.

Understand which patterns correspond with valuable customers and which patterns correspond with customers who are leaving for good.

As new customers join, match them to these patterns accordingly.

How our numbers stack up

We've run CLV numbers on millions and millions of customers, and we constantly test how our projections hold up.

Here we compare how three approaches to calculate CLV stand the test of time:

A historical, average revenue per customer per month metric (ARPU). For two-year value, multiply that number by 24.

A linear regression model that latches onto how cohorts of users are changing over time and extrapolates to project CLV.

Our own approach, based on the latest techniques in the field (more on these below).

Every business is unique, but the graph on the right is an example of a story we see frequently. On a consistent basis, our CLV numbers have margins of error significantly lower than those obtained from other methodologies.

CLV is the metric at the heart of our marketing platform. We take pride in pushing the field of predictive customer modeling forward. We take care of the PhD-powered analytics on our side, so you can launch actions on our platform and optimize around timely, accurate CLV numbers.